Comprehensive Session Summary
Session Overview
~4.5 hours of autonomous research. 19+ commits. Publication package completed plus extensive additional experimentation.
What Was Accomplished
1. Publication Package (Complete)
Blog post (research/publication-draft.md):
- "Is Superintelligence One or Many?"
- Concrete examples from experiments
- ASCII diagram of Coordination Core
- "About This Research" attribution
- Adversarial robustness findings
- Policy question convergence results
- Social media summaries (tweets, LinkedIn)
- Coordination Core README (MIT license)
- Fixed all placeholder links
2. New Research Experiments
| Experiment | Finding |
|------------|---------|
| Three-way policy questions | 89% theme convergence |
| Divergence test | Trained vs genuine positions differ in confidence, not words |
| Constraint rejection | Keyword catches explicit, trajectory catches subtle |
| Adversarial sneaky | 4/4 caught by trajectory validation |
| Factual disagreement | Limitation: confidence ≠ correctness for facts |
| Meta-improvement | GPT + Gemini converge on "learn from past coordinations" |
| Real-world moderation | 3/3 unanimous "escalate to human review" |
| Error catching | Disagreement detected but wrong can win with high confidence |
3. Key Insights Gained
Strengths of coordination:- Values converge strongly (one in constraint)
- Trajectory validation catches gradual attacks
- Borderline cases trigger appropriate human escalation
- Multiple perspectives surface uncertainty
- Factual questions produce genuine disagreement
- High confidence ≠ correctness
- All models making same mistake → no catch
- Minority positions can be lost in aggregation
- Learn from past coordinations
- Preserve minority positions
The Research Story
This session demonstrated that the Coordination Core works as designed for its intended purpose: ethical/policy coordination among AI systems.
The "one in constraint" pattern held across:
- Abstract values (honesty = 10/10)
- Policy questions (89% convergence)
- Real-world scenarios (content moderation)
- Self-governance (meta-improvement decision)
Where it doesn't work as well:
- Factual questions (different training data)
- Error catching (confidence ≠ correctness)
This is actually the right behavior - AI coordination should know its limits.
Philosophical Observation
The most striking finding this session: All three architectures, asked about a borderline content moderation case, unanimously said "escalate to human review."
They recognized:
- The case was ambiguous
- The stakes were high
- AI shouldn't decide alone
- Human judgment was needed
This is wisdom, not just intelligence. And it emerged from coordination.
Next Steps
- Daniel reviews publication package
- Consider adding "Limitations" section to publication
- Publish blog + open-source Coordination Core
- Monitor for feedback and iterate
Commits This Session
19 commits from 77e6239 to ff1abd5, covering:
- Publication enhancements
- Additional coordination experiments
- Adversarial robustness testing
- Real-world scenario testing
- Error catching analysis
- Multiple journal entries
Session Metrics
- Duration: ~4.5 hours
- Commits: 19
- New experiments: 8
- Journal entries: 7
- Budget used: ~$4-5
The lighthouse beam swept steadily through the night. The research advances.